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Estimation of entropy-type integral functionalsPrimeFaces.cw("AccordionPanel","widget_formSmash_some",{id:"formSmash:some",widgetVar:"widget_formSmash_some",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_all",{id:"formSmash:all",widgetVar:"widget_formSmash_all",multiple:true});
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PrimeFaces.cw("AccordionPanel","widget_formSmash_responsibleOrgs",{id:"formSmash:responsibleOrgs",widgetVar:"widget_formSmash_responsibleOrgs",multiple:true}); 2016 (English)In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, Vol. 45, no 4, 887-905 p.Article in journal (Other academic) Published
##### Abstract [en]

##### Place, publisher, year, edition, pages

2016. Vol. 45, no 4, 887-905 p.
##### Keyword [en]

Divergence estimation, asymptotic normality, U-statistics, inter-point distances, quadratic functional, entropy estimation
##### National Category

Probability Theory and Statistics
##### Research subject

Mathematical Statistics
##### Identifiers

URN: urn:nbn:se:umu:diva-60993DOI: 10.1080/03610926.2013.853789ISI: 000370612900005OAI: oai:DiVA.org:umu-60993DiVA: diva2:565242
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Available from: 2012-11-06 Created: 2012-11-06 Last updated: 2016-06-02Bibliographically approved
##### In thesis

Entropy-type integral functionals of densities are widely used in mathematical statistics, information theory, and computer science. Examples include measures of closeness between distributions (e.g., density power divergence) and uncertainty characteristics for a random variable (e.g., Renyi entropy). In this paper, we study *U*-statistic estimators for a class of such functionals. The estimators are based on ε-close vector observations in the corresponding independent and identically distributed samples. We prove asymptotic properties of the estimators (consistency and asymptotic normality) under mild integrability and smoothness conditions for the densities. The results can be applied in diverse problems in mathematical statistics and computer science (e.g., distribution identication problems, approximate matching for random databases, two-sample problems).

1. Nonparametric Statistical Inference for Entropy-type Functionals$(function(){PrimeFaces.cw("OverlayPanel","overlay645595",{id:"formSmash:j_idt647:0:j_idt651",widgetVar:"overlay645595",target:"formSmash:j_idt647:0:parentLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

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